{Unsupervised Hierarchical Image Segmentation based on the TS-MRF model and Fast Mean-Shift Clustering},

year

=

{2008},

month

=

{août},

booktitle

=

{Proc. European Signal Processing Conference (EUSIPCO)},

address

=

{Lausanne, Switzerland},

pdf

=

{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7080521},

keyword

=

{Segmentation, Markov Random Fields, Mean Shift, Land Classification}

}

Abstract :

Tree-Structured Markov Random Field (TS-MRF) models have been recently proposed to provide a hierarchical multiscale description of images. Based on such a model, the unsupervised image segmentation is carried out by means of a sequence of nested class splits, where each class is modeled as a local binary MRF.
We propose here a new TS-MRF unsupervised segmentation technique which improves upon the original algorithm by selecting a better tree structure and eliminating spurious classes. Such results are obtained by using the Mean-Shift procedure to estimate the number of pdf modes at each node (thus allowing for a non-binary tree), and to obtain a more reliable initial clustering for subsequent MRF optimization. To this end, we devise a new reliable and fast clustering algorithm based on the Mean-Shift technique. Experimental results prove the potential of the proposed method.